Recently, many works have shown impressive robot learning results from diverse, human-collected demonstration datasets [1]}, [2]}, [3]}, [4]}. They underline the importance of scalable robot data collection. Yet, the current standard approach for demonstration collection, human teleoperation, is tedious and costly: tasks need to be demonstrated repeatedly and each operator can control only a single robot at a time. Research in teleoperation has focused on exploring different interfaces, such as VR controllers [5]} and smart phones [1]}, but does not address the aforementioned bottlenecks to scaling data collection. Thus, current teleoperation systems are badly equipped to deliver the scalability required by modern robot learning pipelines.
